docstr: Builds basic inference graph for generator
docstr_tok: ["Builds","basic","inference","graph","for","generator"]
code: def _build_network_graph(self):
      """
      Builds basic inference graph for generator
      """
      with tf.variable_scope("Generator", reuse=tf.AUTO_REUSE):
          # Look up tables
          self.src_emb = tf.nn.embedding_lookup(
              self.src_ten, self.src_ph, name="src_lut"
          )
          self.tgt_emb = tf.nn.embedding_lookup(
              self.tgt_ten, self.tgt_ph, name="tgt_lut"
          )
          # Map them
          self.mapWX = self._mapper(self.src_emb)
          # Concatenate them
          self.X = tf.concat([self.mapWX, self.tgt_emb], 0, name="X")
          # Set target for discriminator
          Y = np.zeros(shape=(2 * self.batch_size, 1), dtype=np.float32)
          # Label smoothing
          Y[: self.batch_size] = 1 - self.smooth_val
          Y[self.batch_size :] = self.smooth_val
          # Convert to tensor
          self.Y = tf.convert_to_tensor(Y, name="Y")
  
imports: from __future__ import print_function, division
  import io
  import os
  import time
  import numpy
  import scipy
  import tensorflow
label_desc: Use image node_id 2 for calling the Generator obj's underlying member method code with example usage: obj._build_network_graph() without return types